code: def evaluate(model, data_loader, device, flip=False, flip_pairs=None):
      if flip:
          assert (
              flip_pairs is not None
          ), "enable flip must provide flip_pairs."
  
      model.eval()
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Test: "
  
      key_metric = EvalCOCOMetric(
          data_loader.dataset.coco, "keypoints", "key_results.json"
      )
      for image, targets in metric_logger.log_every(
          data_loader, 100, header
      ):
          images = torch.stack([img.to(device) for img in image])
  
          # 当使用CPU时，跳过GPU相关指令
          if device != torch.device("cpu"):
              torch.cuda.synchronize(device)
  
          model_time = time.time()
          outputs = model(images)
          if flip:
              flipped_images = transforms.flip_images(images)
              flipped_outputs = model(flipped_images)
              flipped_outputs = transforms.flip_back(
                  flipped_outputs, flip_pairs
              )
              # feature is not aligned, shift flipped heatmap for higher accuracy
              # https://github.com/leoxiaobin/deep-high-resolution-net.pytorch/issues/22
              flipped_outputs[..., 1:] = flipped_outputs.clone()[
                  ..., 0:-1
              ]
              outputs = (outputs + flipped_outputs) * 0.5
  
          model_time = time.time() - model_time
  
          # decode keypoint
          reverse_trans = [t["reverse_trans"] for t in targets]
          outputs = transforms.get_final_preds(
              outputs, reverse_trans, post_processing=True
          )
  
          key_metric.update(targets, outputs)
          metric_logger.update(model_time=model_time)
  
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger)
  
      # 同步所有进程中的数据
      key_metric.synchronize_results()
  
      if utils.is_main_process():
          coco_info = key_metric.evaluate()
      else:
          coco_info = None
  
      return coco_info
  
file_path: deep-learning-for-image-processing/pytorch_keypoint/HRNet/train_utils/train_eval_utils.py
imports: import math
  import sys
  import time
  import torch
  import transforms
  import train_utils.distributed_utils
  from .coco_eval import EvalCOCOMetric
  from .loss import KpLoss
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, data_loader, device, flip, flip_pairs) and returns: coco_info